mlsanityData to 7 Oct 2026

From Statistical to Causal Learning

arXiv 2204.00607 ↗First posted 1 Apr 2022cs.AI cs.LG stat.MLcited by 40

Not on the map. Why?

Abstract (arXiv)

We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 50 of its 167 references that are in the map. First posted on arXiv 1 Apr 2022.

Builds on

50 of 167 references.

References · 50

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2019Invariant Risk Minimizationcited by 1,832
  6. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
  7. 2020Object-Centric Learning with Slot Attentioncited by 865
  8. 2017Counterfactual Fairnesscited by 802
  9. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  10. 2021Self-Supervised Learning with Data Augmentations Provably Isolates Content from Stylecited by 308
  11. 2018Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learningcited by 278
  12. 2013Direct and Indirect Effectscited by 276
38 more
  1. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  2. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  3. 2019Causality for Machine Learningcited by 242
  4. 2019Recurrent Independent Mechanismscited by 219
  5. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  6. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  7. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  8. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  9. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 139
  10. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  11. 2017Causal Consistency of Structural Equation Modelscited by 114
  12. 2018Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustnesscited by 108
  13. 2017Learning Independent Causal Mechanismscited by 104
  14. 2021Independent mechanism analysis, a new concept?cited by 96
  15. 2020A Measure-Theoretic Approach to Kernel Conditional Mean Embeddingscited by 86
  16. 2016Discovering Causal Signals in Imagescited by 79
  17. 2021Real-time gravitational-wave science with neural posterior estimationcited by 76
  18. 2015External Validity: From Do-Calculus to Transportability Across Populationscited by 75
  19. 2020On the Fairness of Causal Algorithmic Recoursecited by 69
  20. 2013Learning Gaussian Networkscited by 62
  21. 2019Causal Discovery with General Non-Linear Relationships Using Non-Linear ICAcited by 58
  22. 2013Causal Discovery from Changescited by 52
  23. 2018Counterfactuals uncover the modular structure of deep generative modelscited by 51
  24. 2021Nonlinear Invariant Risk Minimization: A Causal Approachcited by 42
  25. 2014From Ordinary Differential Equations to Structural Causal Models: the deterministic casecited by 41
  26. 2016Kernel-based Tests for Joint Independencecited by 40
  27. 2020Towards causal generative scene models via competition of expertscited by 37
  28. 2016From Deterministic ODEs to Dynamic Structural Causal Modelscited by 32
  29. 2015Multi-Level Cause-Effect Systemscited by 30
  30. 2021Unsupervised Object Learning via Common Fatecited by 29
  31. 2021Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLPcited by 21
  32. 2017Group invariance principles for causal generative modelscited by 20
  33. 2018Detecting non-causal artifacts in multivariate linear regression modelscited by 16
  34. 2019Semi-Supervised Learning, Causality and the Conditional Cluster Assumptioncited by 15
  35. 2015Telling cause from effect in deterministic linear dynamical systemscited by 12
  36. 2020Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effectscited by 10
  37. 2013Consistency of Causal Inference under the Additive Noise Modelcited by 8
  38. 2015Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representationscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 20; 2024: 5; 2025: 5; 2026: 3.

40 papers.

Sort:

By area:

  1. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105
  2. 2023Learning Linear Causal Representations from Interventions under General Nonlinear Mixingcited by 92
  3. 2022Weakly Supervised Representation Learning with Sparse Perturbationscited by 64
  4. 2022Less Data, More Knowledge: Building Next Generation Semantic Communication Networkscited by 61
  5. 2023Score-based Causal Representation Learning with Interventionscited by 49
  6. 2024Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Modelscited by 29
  7. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  8. 2023Unbiased Scene Graph Generation via Two-stage Causal Modelingcited by 21
  9. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
  10. 2022Backtracking Counterfactualscited by 15
  11. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  12. 2023Causal models in string diagramscited by 14
28 more
  1. 2023Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanismscited by 14
  2. 2025A Causal Adjustment Module for Debiasing Scene Graph Generationcited by 14
  3. 2023Toward a Theory of Causation for Interpreting Neural Code Modelscited by 12
  4. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  5. 2022Conditional Independence Testing via Latent Representation Learningcited by 9
  6. 2024Identifiable Object-Centric Representation Learning via Probabilistic Slot Attentioncited by 9
  7. 2022Causal Inference via Style Transfer for Out-of-distribution Generalisationcited by 8
  8. 2023A Measure-Theoretic Axiomatisation of Causalitycited by 8
  9. 2022Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classificationcited by 7
  10. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  11. 2023Finding emergence in data by maximizing effective informationcited by 4
  12. 2023Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representationscited by 4
  13. 2023When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlookcited by 4
  14. 2023Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneitycited by 3
  15. 2025Causal invariant geographic network representations with feature and structural distribution shiftscited by 3
  16. 2023Enriching Disentanglement: From Logical Definitions to Quantitative Metricscited by 2
  17. 2023Concept-free Causal Disentanglement with Variational Graph Auto-Encodercited by 1
  18. 2023Causality and Explainability for Trustworthy Integrated Pest Managementcited by 1
  19. 2024DICS: Find Domain-Invariant and Class-Specific Features for Out-of-Distribution Generalizationcited by 1
  20. 2025ExMAG: Learning of Maximally Ancestral Graphscited by 1
  21. 2022Language-Based Causal Representation Learningcited by 0
  22. 2024Disentangled Representations for Causal Cognitioncited by 0
  23. 2024Working Paper: Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robotscited by 0
  24. 2025On The Causal Network Of Face-selective Regions In Human Brain During Movie Watchingcited by 0
  25. 2025Identifiable Object Representations under Spatial Ambiguitiescited by 0
  26. 2026Counterfactual Contrastive Analysiscited by 0
  27. 2026I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?cited by 0
  28. 2026Identification of Nonlinear and Dependent Latent Factor Structure through Clique Searchcited by 0